**Aflatoxin Contamination Distribution Among Grains and Nuts**

 Eduardo Micotti da Gloria *University of Sao Paulo – ESALQ, Brazil* 

#### **1. Introduction**

74 Aflatoxins – Detection, Measurement and Control

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Grains (cereals and oilseeds) and nuts in general are subject to mold attack, in preharvest and postharvest. Among molds that can attack these foods *A. flavus*, and *A. parasiticus* are important because they can produce aflatoxins that are considered a potent natural toxin (Wild & Gong, 2010). Aflatoxin can be produced mainly by different *Aspergillus* species, but *Emiricella* and *Petromyces* have been reported as aflatoxin producers (Frisvad et al., 2005).

Aflatoxin contamination has been reported for grains as corn, soya, wheat, rice, and cottonseed, and nuts such as peanuts, almonds, Brazil nuts, hazelnuts, walnuts, cashew nuts, pecans, and pistachio nuts (Fuller et al., 1977; Ayres, 1977; Moss, 2002; CAST, 2003; Gürses, 2006). Despite aflatoxin contamination having been observed in several foodstuffs, the contamination of maize, peanuts, and oilseeds can be considered, in terms of diet exposure, the most important worldwide (Benford et al. (2010).

Based on deleterious problems that aflatoxin can cause to human and animal health, some countries established a maximum concentration for aflatoxins in specific products. According to published data (Van Egmond, 2007), until 2003 one hundred countries had established legal limits for mycotoxins, and most of them regulated the aflatoxins presence in food and feeds.

Several biotic and abiotic factors can determine fungal infection and growth, as well as aflatoxin production in preharvest. Temperature, water availability, plant nutrition, infestation of weeds, birds, and insects, plant density, crop rotation, drought stress, presence of antifungal compounds, fungal load, microbial competition, substrate composition, and mold strain capacity to produce aflatoxin are some important factors. The incidence of these factors is different in preharvest among plants and production areas of the same farm, among different farms of the same region and among different producer regions. Even among grains of the same ear or peanuts of the same pod the differences can occur. In postharvest, factors such as temperature, availability of water, oxygen, and carbon dioxide, insect and rodents infestation, incidence of broken grains or nuts, the cleaning of the product, toxigenic fungal load, microbial competition, antifungal compound presence, and substrate composition are important too. Transport, waiting time for drying, drying system (temperature and drying rate), and storage conditions can affect these factors during the postharvest period (Dorner, 2008; Diener et al., 1987; Campbell et al., 2006; Molyneux et al., 2007).

As a result of variable conditions that can occur during pre and postharvest, the aflatoxin contamination level among grains and nuts within the same lot can have an extremely

Aflatoxin Contamination Distribution Among Grains and Nuts 77

Despite uneven contamination representing a problem for the task of sampling, it consists in an opportunity to segregate aflatoxin contaminated grains and nuts from an entire lot. As contamination is concentrated in few grains or nuts the removal of those material can to

The fungal growth in grains and nuts is normally related to some changes in their biochemical and sometimes in the visual characteristics (Pomenranz, 1992; Wacowicz, 1991). Discoloration or staining of skin or kernel material, appearance of fluorescent material, changes in the standard of reflectance and transmittance spectroscopy, density and size changes in relation to sound grains and nuts are some characteristics that have been observed as consequence of fungal growth (Kumar & Agarwal, 1997; Pomeranz, 1992). Some technologies able to detect and remove grains and nuts with the previously mentioned differences in their characteristics have been studied and used to improve the overall quality of commodities, but their efficiency to be used as a way to reach a reduction of aflatoxin levels in specific commodities must be evaluated. Table 3 shows some technologies which have been studied and used to segregate aflatoxin contamination in lots

Table 3. Examples of technologies studied to improve the overall quality of commodities or

Fungal growth can cause chemical changes in grains or nuts, which can result in some modifications in color or form. The modifications are not always visible to the naked eye, some of them can be visible just with the aid of specific techniques or equipment. Color changes in grains or nuts can appear as a result of biochemical reactions or due to the fungal mycelium itself. According to Robin et al. (1995), hydrolysis of the macromolecules, e.g., proteins, lipids, and polysaccharides, occurs during mold infection, resulting in the release of free amino acids, free fatty acids, and simple sugars. These breakdown products contribute to color development in, e.g., peanut kernels during roasting of blanching before

The detection of fungal changes in grains and nuts makes it possible to know where fungal growth, and probable mycotoxin production, has occurred. As the presence of a fungus does not assure mycotoxin presence (Gloria et al., 2006), some researchers have tried to show correlations between changes in grains or nuts and their mycotoxin concentration. The correlation between poorly graded categories of grains and nuts and aflatoxin concentration has been shown for peanuts, maize, and almonds (Whitaker et al., 1998; Johansson et al.,

The optical detection of faulty grains, nuts, kernel of nuts (blemished, discolored, and misshapen), and gross contaminants (glass, stones, insects, rotten product, extraneous

reduce the aflatoxin levels.

of commodities.

electronic color sorting.

2006; Whitaker et al., 2010).

**Technology Product**

Hand picking Nuts Blanching and electronic color sorting Peanuts

to reduce aflatoxin contamination levels of an entire lot

**2. Segregation by appearance features** 

Electronic color sorting Grains and nuts

Gravimetric table Grains and nuts Size separation Grains and nuts Flotation Maize and peanuts

uneven distribution. The uneven distribution of aflatoxin contamination was observed in different foodstuffs, such as peanuts, maize, almonds, Brazil nuts, and pistachios (Cucullu et al., 1966; Whitaker et al., 1994; Shotwell et al., 1974; Schatzki & Pan, 1996; Steiner et al., 1992; Shade et al. 1975; Ozay et al., 2007). In a contaminated lot, just a few grains and nut kernels can have quite high concentration levels of aflatoxin, and most of them do not have detectable contamination. Table 1 shows some high individual concentrations detected in a peanut, a maize grain, a Brazil nut, in a pistachio, and a cottonseed. The high concentration observed in an individual grain or kernel can result, for example in maize, in a contamination level of 136 µg/kg, when just one grain is contaminated, considering 0.34 g as the average weight of maize grain, and the high concentration showed in the table 1.

The not uniform distribution of contamination within a lot represents a great challenge to measure the true contamination level of the lot. If several samples are collected from the same lot of a commodity, completely different contamination results can be obtained, as shown in table 2. Several theoretical distribution models have been investigated as possible models to describe the observed distribution of aflatoxin test results. Among them, are the negative binomial (Whitaker et al., 1972; Whitaker & Wiser, 1969; Knutti & Schlatter, 1978; Knutti & Schlatter, 1982), compound gamma (Knutti & Schlatter, 1978; Knutti & Schlatter, 1982; Giesbrecht & Whitaker, 1998), log normal (Giesbrecht & Whitaker, 1998; Brown, 1984), truncated normal (Giesbrecht & Whitaker, 1998), Waibel (Waibel, 1977), 3-parameter Weibull (Sharkey et al., 1994; Schatzki, 1995), exponential, chisquare (Tiemstra, 1969), logistic, and Neiman-A (Whitaker et al.,1972). Additionally, evaluations of several sampling plans to detect aflatoxin contamination have been done, and they have shown, with some differences due to plan characteristics and product to be sampled, that results obtained by sampling plans always involve a certain degree of uncertainty (Whitaker et al., 2005b).


Table 1. Concentration reported for individual grain or nut


Source: Dickens and Whitaker (1986)

Table 2. Example of variation that can be observed among sample results when a peanut lot is sampled

uneven distribution. The uneven distribution of aflatoxin contamination was observed in different foodstuffs, such as peanuts, maize, almonds, Brazil nuts, and pistachios (Cucullu et al., 1966; Whitaker et al., 1994; Shotwell et al., 1974; Schatzki & Pan, 1996; Steiner et al., 1992; Shade et al. 1975; Ozay et al., 2007). In a contaminated lot, just a few grains and nut kernels can have quite high concentration levels of aflatoxin, and most of them do not have detectable contamination. Table 1 shows some high individual concentrations detected in a peanut, a maize grain, a Brazil nut, in a pistachio, and a cottonseed. The high concentration observed in an individual grain or kernel can result, for example in maize, in a contamination level of 136 µg/kg, when just one grain is contaminated, considering 0.34 g as

the average weight of maize grain, and the high concentration showed in the table 1.

**Product Aflatoxin b1 concentration reported (µg/kg) Reference** 

Table 1. Concentration reported for individual grain or nut

Brazil nuts 4,000 Steiner et al. (1992) Brazil nuts 25,000 Stoloff et al. (1969) Pistachio nuts 1,400,000 Steiner et al. (1992) Peanuts 1,100,00 Cucullu et al. (1966) Maize 400,000 Shotwell et al. (1974) Cottonseed 5,750,000 Cucullu et al. (1977)

**Lot number Aflatoxin analysis (µg/Kg) Average**  1 0 0 0 0 8 8 15 16 16 125 19 2 0 0 0 0 0 0 0 8 22 198 22 3 0 0 0 0 0 0 0 9 12 285 31 4 5 12 56 66 70 92 98 132 141 164 84 5 18 50 53 72 82 108 112 127 182 191 100 6 29 37 41 71 95 117 168 174 183 197 111

Table 2. Example of variation that can be observed among sample results when a peanut lot

uncertainty (Whitaker et al., 2005b).

Source: Dickens and Whitaker (1986)

is sampled

The not uniform distribution of contamination within a lot represents a great challenge to measure the true contamination level of the lot. If several samples are collected from the same lot of a commodity, completely different contamination results can be obtained, as shown in table 2. Several theoretical distribution models have been investigated as possible models to describe the observed distribution of aflatoxin test results. Among them, are the negative binomial (Whitaker et al., 1972; Whitaker & Wiser, 1969; Knutti & Schlatter, 1978; Knutti & Schlatter, 1982), compound gamma (Knutti & Schlatter, 1978; Knutti & Schlatter, 1982; Giesbrecht & Whitaker, 1998), log normal (Giesbrecht & Whitaker, 1998; Brown, 1984), truncated normal (Giesbrecht & Whitaker, 1998), Waibel (Waibel, 1977), 3-parameter Weibull (Sharkey et al., 1994; Schatzki, 1995), exponential, chisquare (Tiemstra, 1969), logistic, and Neiman-A (Whitaker et al.,1972). Additionally, evaluations of several sampling plans to detect aflatoxin contamination have been done, and they have shown, with some differences due to plan characteristics and product to be sampled, that results obtained by sampling plans always involve a certain degree of Despite uneven contamination representing a problem for the task of sampling, it consists in an opportunity to segregate aflatoxin contaminated grains and nuts from an entire lot. As contamination is concentrated in few grains or nuts the removal of those material can to reduce the aflatoxin levels.

The fungal growth in grains and nuts is normally related to some changes in their biochemical and sometimes in the visual characteristics (Pomenranz, 1992; Wacowicz, 1991). Discoloration or staining of skin or kernel material, appearance of fluorescent material, changes in the standard of reflectance and transmittance spectroscopy, density and size changes in relation to sound grains and nuts are some characteristics that have been observed as consequence of fungal growth (Kumar & Agarwal, 1997; Pomeranz, 1992).

Some technologies able to detect and remove grains and nuts with the previously mentioned differences in their characteristics have been studied and used to improve the overall quality of commodities, but their efficiency to be used as a way to reach a reduction of aflatoxin levels in specific commodities must be evaluated. Table 3 shows some technologies which have been studied and used to segregate aflatoxin contamination in lots of commodities.


Table 3. Examples of technologies studied to improve the overall quality of commodities or to reduce aflatoxin contamination levels of an entire lot
